Titlebook: Emerging Trends in Visual Computing; LIX Fall Colloquium, Frank Nielsen Book 2009 Springer-Verlag Berlin Heidelberg 2009 3-manifold.3D cine

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過于光澤

Discrete Curvature Flows for Surfaces and 3-Manifoldsds that are recently introduced into the engineering fields: the discrete Ricci flow and discrete Yamabe flow for surfaces with various topology, and the discrete curvature flow for hyperbolic 3-manifolds with boundaries. For each flow, we introduce its theories in both the smooth setting and the di

minion

Meditate

Computational Geometry from the Viewpoint of Affine Differential Geometrytudy hypersurfaces in an affine space that are invariant under the action of the group of affine transformations. Since incidence relations (configurations of vertexes, edges, etc.) in computational geometry are invariant under affine transformations, we may say that affine differential geometry giv

collateral

OTTER

Clustering Multivariate Normal Distributionsof clusters using a generalization of Lloyd’s .-means algorithm [1]. We revisit this information-theoretic clustering problem under the auspices of mixed-type Bregman divergences, and show that the approach of Davis and Dhillon [2] (NIPS*06) can also be derived directly, by applying the Bregman .-me

OTTER

原告

Shape from Depth Discontinuitiesspace of oriented 3D lines, or ., as opposed to the traditional dual space of planes. An image capture process detects points on a depth discontinuity sweep from a camera moving with respect to an object, or from a static camera and a moving object. A depth discontinuity sweep is a surface in dual s

BRIBE

Computational Photography: Epsilon to Coded Photographytional cameras, enables novel imaging applications and simplifies many computer vision tasks. However, a majority of current Computational photography methods involves taking multiple sequential photos by changing scene parameters and fusing the photos to create a richer representation. Epsilon phot

Hallowed

Unifying Subspace and Distance Metric Learning with Bhattacharyya Coefficient for Image Classificatio the local distribution of data, we divide the .-nearest neighbors of each sample into the intra-class set and the inter-class set, and we aim to learn a distance metric in the embedding subspace, which can make the distances between the sample and its intra-class set smaller than the distances bet